Algorithm and a method for characterizing surfaces with fractal nature

ABSTRACT

A computer implemented method for directly determining parameters defining a Weierstrass-Mandelbrot (W-M) analytical representation of a rough surface scalar field with fractal character, embedded in a three dimensional space, utilizing pre-existing measured elevation data of a rough surface in the form of a discrete collection of data describing a scalar field at distinct spatial coordinates, is carried out by applying an inverse algorithm to the elevation data to thereby determine the parameters that define the analytical and continuous W-M representation of the rough surface. The invention provides a comprehensive approach for identifying all parameters of the W-M function including the phases and the density of the frequencies that must greater than 1. This enables the infinite-resolution analytical representation of any surface or density array through the W-M fractal function.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application61/527,251 filed on Aug. 25, 2011, incorporated herein by reference.

FIELD OF THE INVENTION

The invention is directed to characterizing a surface with a fractalnature, and more particularly to directly determine parameters defininga Weierstrass-Mandelbrot (W-M) analytical representation of a roughsurface scalar field with fractal character.

BACKGROUND OF THE INVENTION

Although fractal modeling of rough surfaces in contact was motivated byinadequacies in the early theory and applications of tribology, there isa plethora of problems that can be benefited from this type of modeling.Usually these are problems that require an estimate of materialproperties across interfaces between dissimilar materials, although manyother areas such as sonic and electromagnetic scattering as well asimage processing applications can be benefited by fractal modeling.Within the context of material science, it has already been demonstratedthat such a modeling approach is useful for contact of deformablebodies, temperature distribution, friction, thermal contact conductance,and electric resistance.

The Weierstrass-Mandelbrot (W-M) function is employed for surfaceparametrization and in one approach is a multivariate analyticgeneralization of its univariate version and has wide applicability inall applications requiring an analytical description of a rough surface.There has been a considerable effort in establishing methodologies ofdetermining just a single parameter describing surface representationsand that is the fractal dimension.

The most widely used method for the determination of the fractaldimension—as one of the parameters characterizing the surface underinvestigation—, regardless of the particular analytical model, is thepower spectrum method. However, beyond the obvious weakness that onlyone of the parameters of the fractal surface can be identified by thismethod (i.e. the fractal dimension), the method has proven not to beaccurate enough and it also enforces assumptions requiring a prioridefinition of the rest of the parameters that can be very restrictive ornot always true.

Surface Modeling

It has been established that a W-M function is s very rich analyticalrepresentation that can model surface topographies of fractal naturesuch as material surfaces at small scales. This seems to be trueespecially because of the properties of continuity,non-differentiability and self affinity of specific types of fractals,that are also desired properties of surface topographies. The surfacepower spectra obeys a power-law relationship over a wide range offrequencies, because the surface topographies resemble a random process.Such a surface can be represented by a complex function W as:

$\begin{matrix}{{W(x)} = {\sum\limits_{n = {- \infty}}^{\infty}{{\gamma^{{({D - 2})}n}\left( {1 - ^{\; \gamma^{n_{x}}}} \right)}^{\; \varphi_{n}}}}} & (1)\end{matrix}$

where x is a real variable. A two dimensional profile can be obtainedfrom the real part of Eq. 1:

$\begin{matrix}{{z(x)} = {{{Re}\left\lbrack {W(x)} \right\rbrack} = {\sum\limits_{n = {- \infty}}^{\infty}{{\gamma^{{({D - 2})}n}\left\lbrack {{\cos \; \varphi_{n}} - {\cos \left( {{\gamma^{n}x} + \varphi_{n}} \right)}} \right\rbrack}.}}}} & (2)\end{matrix}$

The relevant parameters here are defined as follows: D is the fractaldimension (1<D<2 for line profiles), φ_(n) is a random phase that isused to prevent coincidence of different phases, n is the frequencyindex and γ is a parameter that controls the density of the frequenciesand must be greater than 1; γ usually takes values in the vicinity of1.5 because of surface flatness and frequency distribution densityconsiderations. The later though has been recently debated and only therequirement γ>1 was considered as a valid assumption.

A three-dimensional fractal surface that exhibits randomness is thetwo-variable W-M function that is given by:

$\begin{matrix}{{z\left( {r,\theta} \right)} = {\sqrt{\left( \frac{\ln \; \gamma}{M} \right)}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = {- \infty}}^{\infty}{\left( {k\; \gamma^{n}} \right)^{D - 3}\left\{ {{\cos \; \varphi_{mn}} - {\cos \left\lbrack {{k\; \gamma^{n}r\; {\cos \left( {\theta - \alpha_{m}} \right)}} + \varphi_{mn}} \right\rbrack}} \right\}}}}}} & (3)\end{matrix}$

where M is the number of superimposed ridges, D now takes values between2 and 3, α_(m) is an arbitrary angle that is used to offset the ridgesin the azimuthal direction and is equal to πm/M for equally offsetridges. k is the wave number and is given by: k=2 π/L, L is the size ofthe sample. In practice the upper limit of n is not infinite and isgiven by:

N=n _(max)=int[log(L/Lc)/log γ]  (4)

with L_(c) being a cut-off wavelength, typically defined either by thehighest sampling frequency, or by a physical barrier like theinteratomic distance of the surface atoms. Since the lowest frequency is1/L, the lowest limit of n can be set equal to 0. Finally the cartesiancoordinates (x,y) are mapped to polar coordinates (r,θ) according to:

$\begin{matrix}{{r = \sqrt{x^{2} + y^{2}}},{\theta = {\tan^{- 1}\left( \frac{y}{x} \right)}}} & (5)\end{matrix}$

If we substitute the previous relationships of A_(m), α_(m), r, θ, k andthe limits of n, in Eq. 3 we get:

$\begin{matrix}{{z\left( {x,y} \right)} = {{A\left( \frac{L}{2\pi} \right)}^{3 - D}\sqrt{\frac{\ln \; \gamma}{M}}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = 0}^{N}\; {\gamma^{{({D - 3})}n}\left\{ {\cos \; {\varphi_{mn} \cdot {--{\cos \left\lbrack {{\frac{2\pi \; \gamma^{n}\sqrt{x^{2} + y^{2}}}{L}{\cos \left( {{\tan^{- 1}\left( \frac{y}{x} \right)} - \frac{\pi \; m}{M}} \right)}} + \varphi_{mn}} \right\rbrack}}}} \right.}}}}} & (6)\end{matrix}$

Parameter A can be substituted by 2π(2 π/G)^(2-D), and therefore Eq. 6becomes:

$\begin{matrix}{{z\left( {x,y} \right)} = {{L\left( \frac{G}{L} \right)}^{D - 2}\sqrt{\frac{\ln \; \gamma}{M}}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = 0}^{N}\; {\gamma^{{({D - 3})}n}\left\{ {\cos \; {\varphi_{mn} \cdot {--{\cos \left\lbrack {{\frac{2\pi \; \gamma^{n}\sqrt{x^{2} + y^{2}}}{L}{\cos \left( {{\tan^{- 1}\left( \frac{y}{x} \right)} - \frac{\pi \; m}{M}} \right)}} + \varphi_{mn}} \right\rbrack}}}} \right.}}}}} & (7)\end{matrix}$

The parameter G is independent of the frequency and is referred to asthe fractal roughness.

Although the surface representation of Eq. 7 is in a generallyconvenient form for computations and phenomenological observations, itis still not in a form that can be used to identify the phases φ_(mn).In order to achieve phase identification for a given set of topographicor elevation data, we need to use an expression that decouples thephases from the other variables in the function. Such a refactoredrepresentation exists and in the 2 dimensional case is expressed by thecomplex function:

$\begin{matrix}\begin{matrix}{{W(r)} = {W\left( {r,\theta} \right)}} \\{= {\sqrt{\frac{\ln \; \gamma}{M}}{\sum\limits_{m = 1}^{M}{A_{m}{\sum\limits_{n = {- \infty}}^{\infty}{\left( {–}^{\; k_{0}\gamma^{n}r\; {\cos {({\theta - a_{m}})}}} \right){^{{\varphi}_{mn}}\left( {k_{0}\gamma^{n}} \right)}^{D - 3}}}}}}}\end{matrix} & (8)\end{matrix}$

Performing the same substitutions with those in Eqs. 3, 6 and 7, theprevious relationship takes the refactored form:

$\begin{matrix}{{W\left( {r,\theta} \right)} = {{L\left( \frac{G}{L} \right)}^{D - 2}\sqrt{\frac{\ln \; \gamma}{M}}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = 0}^{N}{{\gamma^{{({D - 3})}n}\left( {1{–}^{\; \frac{2\pi}{L}\gamma^{n}r\; {\cos {({\theta - a_{m}})}}}} \right)}^{{\varphi}_{mn}}}}}}} & (9)\end{matrix}$

The parameters in this equation have the exact same meaning as theparameters in Eq. 3.

Next, assume that for a given set of parameters, a surface is describedby Eq. 9. For another set of the D and G parameters D′ and G′ we seek tocalculate the new phases, so that the new and the original surfacescoincide:

W(r,θ)=W′(r,θ),∀r,θεR  (10)

or:

$\begin{matrix}{\mspace{79mu} {{{L\left( \frac{G}{L} \right)}^{D - 2}\sqrt{\frac{\ln \; \gamma}{M}}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = 0}^{N}{{\gamma^{{({D - 3})}n}\left( {1 - ^{\frac{2\pi}{L}\gamma^{n}{{rcos}{({\theta - a_{m}})}}}} \right)}^{{\varphi}_{mn}}}}}}=={{L\left( \frac{G^{\prime}}{L} \right)}^{D^{\prime} - 2}\sqrt{\frac{\ln \; \gamma}{M}}{\sum\limits_{m = 1}^{M}\mspace{20mu} {\sum\limits_{n = 0}^{N}{{\gamma^{{({D^{\prime} - 3})}n}\left( {1 - ^{\frac{2\pi}{L}\gamma^{n}{{rcos}{({\theta - a_{m}})}}}} \right)}^{{\varphi}_{mn}^{\prime}}}}}}}} & (11)\end{matrix}$

For Eq. 11 to hold for all r, θεR it must also hold that all the addedin parameters of the sums be equal. Since the

${c\left( {r,\theta} \right)} = \left( {1 - ^{\frac{2\pi}{L}\gamma^{n}{{rcos}{({\theta - a_{m}})}}}} \right)$

expressions on the left and right side of Eq. 11 are equal, it musttherefore hold:

$\begin{matrix}{{{L\left( \frac{G}{L} \right)}^{D - 2}\sqrt{\frac{\ln \; \gamma}{M}}\gamma^{{({D - 3})}n}^{{\varphi}_{mn}}} = {{L\left( \frac{G^{\prime}}{L} \right)}^{D^{\prime} - 2}\sqrt{\frac{\ln \; \gamma}{M}}\gamma^{{({D^{\prime} - 3})}n}^{{\varphi}_{mn}^{\prime}}}} & (12)\end{matrix}$

Solving Eq. 12 for φ′_(mn):

$\begin{matrix}{\varphi_{mn}^{\prime} = {{2\pi \; v} - {i\; {\ln\left\lbrack {{^{{\varphi}_{mn}}\left( \frac{G}{L} \right)}^{D - 2}{\gamma^{n{({D - D^{\prime}})}}\left( \frac{G^{\prime}}{L} \right)}^{2 - D^{\prime}}} \right\rbrack}}}} & (13)\end{matrix}$

with vεZ an arbitrary number. Clearly, the fact that we can always finda new set of values for the phases, for any combination of the fractalparameters D and G indicates that we can always find a surface,independent of the magnitudes of those two parameters. From acharacterization perspective this means that these two parameters can beselected to be known, since the phases can adjust for different choicesof D and G. This finding has serious implications on all physics-basedmodels that are based on parameters D and G, because we havedemonstrated that alternative values for D and G can be used if analternative but consistent set of phases is established.

It would therefore be desirable to provide a method for determining allof the critical parameters involved in the full specification of the W-Mfunction.

BRIEF SUMMARY OF THE INVENTION

According to the invention, a computer implemented method for directlydetermining parameters defining a Weierstrass-Mandelbrot (W-M)analytical representation of a rough surface scalar field with fractalcharacter, embedded in a three dimensional space, utilizing pre-existingmeasured elevation data of a rough surface in the form of a discretecollection of data describing a scalar field at distinct spatialcoordinates, is carried out by applying an inverse algorithm to theelevation data to thereby determine the parameters that define theanalytical and continuous W-M representation of the rough surface.

The invention provides an approach that enables the determination of themechanical, thermal, electrical and fluid properties of the slidingcontact between two different conductors of heat and electric current incontact, under simultaneous mechanical loading as it transitions from astatic, to low velocity, up to high velocity and as phase transformationoccurs. The invention also enables the determination of the initialproperties of this evolutionary staging as it relates to the staticcontact case from simple profilometric data. Overall, the inventiondetermines all of the parameters that control the parametricrepresentation of a surface possessing both a random and fractalcharacter.

The invention provides a comprehensive approach for identifying allparameters of the W-M function including the phases and the density ofthe frequencies that must greater than 1. This enables theinfinite-resolution analytical representation of any surface or densityarray through the W-M fractal function.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1A is a block diagram showing the forward problem perspective andFIG. 1B is a block diagram showing the inverse problem perspective Aaccording to the invention;

FIGS. 2A-B respectively show pseudo-colored density plots of syntheticsurfaces and inversely identified surfaces of a W-M fractal according tothe invention;

FIG. 3 shows the pseudo-colored density plots of the absolute differencebetween reference and identified surfaces according to the invention(note that the order of magnitude is 10⁻¹⁵);

FIG. 4 is a graph of the mean error of inverse identification through vsparameter γ according to the invention;

FIGS. 5A-B respectively show pseudo-colored density plots of Aluminum6061T6 profilometric data and inversely identified aluminum surface fora fractal with M×N=25×25 according to the invention;

FIG. 6 shows pseudo-colored density plots of the percentage errordifferenc between the surfaces of FIGS. 5A and 5B according to theinvention;

FIG. 7 is a graph showing the mean error of inverse identificationthrough Singular Value Decomposition (SVD) vs parameter γ for thealuminum surface and M×N=25×25 according to the invention;

FIGS. 8A-B respectively show pseudo-colored density plots of Aluminum6061T6 profilometric data and inversely identified aluminum surface fora fractal with M×N=50×50 according to the invention;

FIG. 9 is a graph showing the mean error of inverse identificationthrough SVD vs. parameter γ for the aluminum surface and M×N=50×50according to the invention;

FIGS. 10A-B respectively show Bathymetry data: Kane Fracture Zone andAnalytical Reconstruction of Kane Fracture Zone Bathymetry according tothe invention; and

FIGS. 11A-B respectively show Image data: Glacier Canyon and AnalyticalReconstruction of Glacier Canyon Image according to the invention;

DETAILED DESCRIPTION OF THE INVENTION The Inverse Problem

The systemic view of both the forward and the inverse problem is shownin FIGS. 1A-B. In the case of the inverse problem, it is shown that itssolution produces the parameters of the fractal surface functionprovided that the topography or profilometric values of the surfacez(x,y) have been experimentally determined and therefore are known.

More specifically, given an array of elevation or height measurementsz_(ij) ^(e), i, j=1 . . . K over a square region of size L×L we aim atidentifying the parameters γ and φ_(mn) of a surface z(x,y) that bestfits those measurements. In previous studies the identification of thoseparameters was done by defining a global optimization problem andnumerically identifying those parameters via a Monte-Carlo optimizer.However, it is obvious that if the purpose is to also identify thephases, the previous approach is computationally unacceptable because ofthe dimensionality of the inverse problem that makes it computationallyinefficient to the point of intolerability.

In the following, the surface represented by the fractal isreformulated, so that it takes a form appropriate for the inverseidentification of the phases.

The inner sum in Eq. 9 can be substituted by the product:

$\begin{matrix}{{\sum\limits_{n = 0}^{N}{{\gamma^{{({D - 3})}n}\left( {1 - ^{\frac{2\pi}{L}\gamma^{n}{{rcos}{({\theta - a_{m}})}}}} \right)}^{{\varphi}_{mn}}}} = {{c_{m}\left( {r,\theta} \right)}^{T}f_{m}}} & (14)\end{matrix}$

where c_(m)(r,θ) and f_(m) are vectors in R^((N+1)) given by:

$\begin{matrix}{{c_{m}\left( {r,\theta} \right)} = {\left\{ {c_{m\; 0},c_{m\; 1},\ldots \mspace{14mu},c_{mN}} \right\}^{T} = \left\{ {{\gamma^{{({D - 3})}0}\left( {1 - ^{\frac{2\pi}{L}\gamma^{0}{{rcos}{({\theta - a_{m}})}}}} \right)},{\gamma^{{({D - 3})}1}\left( {1 - ^{\frac{2\pi}{L}\gamma^{1}{{rcos}{({\theta - a_{m}})}}}} \right)},{\ldots {\gamma^{{({D - 3})}N}\left( {1 - ^{\frac{2\pi}{L}\gamma^{1}{{rcos}{({\theta - a_{m}})}}}} \right)}^{T^{\;}}}} \right.}} & (15)\end{matrix}$

and:

f _(m) ={e ^(iφ) ^(m0) ,e ^(iφ) ^(mN) }^(T)  (16)

By setting:

$\begin{matrix}{{\,^{*}Q} = {{L\left( \frac{G}{L} \right)}^{D - 2}\sqrt{\frac{\ln \; \gamma}{M}}}} & (17)\end{matrix}$

Eq. 9 can be written as:

$\begin{matrix}{{W\left( {r,\theta} \right)} = {{\,^{*}Q}{\sum\limits_{m = 1}^{M}{c_{m}^{T}f_{m}}}}} & (18)\end{matrix}$

By coalescing the vectors c_(m) and f_(m) into larger vectors inR^(M(N+1)) such as:

c(r,θ)={c ₁ ^(T) , c ₂ ^(T) , . . . , c _(M) ^(T)}^(T)  (19)

and

f={f ₁ ^(T) , f ₂ ^(T) , . . . , f _(M) ^(T)}^(T)  (20)

Eq. 18 can be written as:

W(r,θ)=*Qc(r,θ)^(T) f  (21)

For the needs of the inverse problem characterization we assume that anumber of measurements at points r_(j) ^(e)={r_(j),θ_(j)}^(T) exist fora surface represented as z^(e)(r_(j) ^(e))=z^(e)(r_(j),θ_(j)), j=1 . . .K, K≧M(N+1). We seek to identify a surface that is described by Eq. 21and approximates the experimental points z^(e)(r_(j),θ_(j)). To solvethis problem we first form the following linear system:

$\begin{matrix}{{\begin{bmatrix}{W\left( {r_{1},\theta_{1}} \right)} \\{W\left( {r_{2},\theta_{2}} \right)} \\\vdots \\{W\left( {r_{K},\theta_{K}} \right)}\end{bmatrix} = \begin{bmatrix}{z^{e}\left( {r_{1},\theta_{1}} \right)} \\{z^{e}\left( {r_{2},\theta_{2}} \right)} \\\vdots \\{z^{e}\left( {r_{K},\theta_{K}} \right)}\end{bmatrix}}{{or}\text{:}}} & (22) \\{{{{\,^{*}Q}\begin{bmatrix}{c\left( {r_{1},\theta_{1}} \right)}^{T} \\{c\left( {r_{2},\theta_{2}} \right)}^{T} \\\ldots \\{c\left( {r_{K},\theta_{K}} \right)}^{T}\end{bmatrix}}\begin{bmatrix}f_{1} \\f_{2} \\\ldots \\f_{M}\end{bmatrix}} = \begin{bmatrix}{z^{e}\left( {r_{1},\theta_{1}} \right)} \\{z^{e}\left( {r_{2},\theta_{2}} \right)} \\\vdots \\{z^{e}\left( {r_{K},\theta_{K}} \right)}\end{bmatrix}} & (23)\end{matrix}$

If the vectors in Eq. 22 are expanded, the system can be written as:

Cp=z  (24)

with:

$C = {{\,^{*}Q}\begin{bmatrix}{{c_{11}\left( {r_{1},\theta_{1}} \right)},{c_{12}\left( {r_{1},\theta_{1}} \right)},\ldots \mspace{14mu},{c_{21}\left( {r_{1},\theta_{1}} \right)},\ldots \mspace{14mu},{c_{MN}\left( {r_{1},\theta_{1}} \right)}} \\{{c_{11}\left( {r_{2},\theta_{2}} \right)},{c_{12}\left( {r_{2},\theta_{2}} \right)},\ldots \mspace{14mu},{c_{21}\left( {r_{2},\theta_{2}} \right)},\ldots \mspace{14mu},{c_{MN}\left( {r_{2},\theta_{2}} \right)}} \\\vdots \\{{c_{11}\left( {r_{K},\theta_{K}} \right)},{c_{12}\left( {r_{K},\theta_{K}} \right)},\ldots \mspace{14mu},{c_{21}\left( {r_{k},\theta_{K}} \right)},\ldots \mspace{14mu},{c_{MN}\left( {r_{K},\theta_{K}} \right)}}\end{bmatrix}}$   p = [φ₁₁, φ₁₂, …  , φ_(1N), φ₂₁, …  , φ_(MN)]^(T)  and $\mspace{20mu} {z = \begin{bmatrix}{z^{e}\left( {r_{1},\theta_{1}} \right)} \\{z^{e}\left( {r_{2},\theta_{2}} \right)} \\\vdots \\{z^{e}\left( {r_{K},\theta_{K}} \right)}\end{bmatrix}}$

The system of Eq. 23 is an overdetermined system of M(N+1) equations.Since the right hand side vector z contains experimental measurements,it also contains noise; the system cannot, in general, have an exactsolution. Nevertheless, we can seek a p, such as PCp−z P is minimized,where P◯P is the vector norm. Such a p is known as the least squaressolution to the over-determined system. It should be noted that the lefthand side expression of Eq. 22 yields results in the complex domain, butas long as a minimal solution is achieved for real numbers on the righthand side, the imaginary parts will be close to 0. A solution can begiven by the following equation:

p=Vy  (25)

where V is calculated by the Singular Value Decomposition (SVD) of C as:

UDV ^(T) =C  (26)

where y is a vector defined as y_(i)=b_(i)′/d_(i), b={b_(i)} is a vectorgiven by:

b=U ^(T) p  (27)

and d_(i) is the ith entry of the diagonal of D.

The solution of the inverse problem as described by the overdeterminedsystem of Eq. 22 gives the phases φ_(mn) given known values of the otherparameters. In a general surface the only other parameter that isunknown is γ. It is evident from Eq. (13) that parameters G and D don'tneed to be considered as unknowns to be determined in this optimization.This is because for any combination of the phases it is always possibleto find new values for φ_(mn), that result in generating the samesurface as was demonstrated earlier.

Furthermore, L_(c) can be chosen arbitrarily and with the intend toincrease the number of phases participating in the reconstruction of thesurface, we can always arbitrarily choose a number for the N or viceversa. Of course the higher the number of M and N the better the surfacewill be approximated. We have found that practically an upper limit ofthose parameters that gives satisfactory results is that of the size ofthe approximated dataset.

Numerical Experiments

In order to assess the quality of the surface characterization resultsof the numerical examples that follow, we define the following errorfunction:

$\begin{matrix}{{error} = \frac{\sum\limits_{i = 1}^{P}{\frac{z_{i}^{e} - z_{i}^{d}}{{\max \left( z_{i} \right)}^{d} - {\min \left( z_{i}^{d} \right)}}}}{P}} & (28)\end{matrix}$

where z_(i) ^(e) is the elevation of the experimentally measured (orreference) points, P is the number of those points. In the followingexamples P is set as P=K×K=50×50=2500 points. z_(i) ^(d) is theelevation of the inversely identified surface and is equal to the realpart of the truncated W-M function 9 (z_(i) ^(d)=Re{W(r,θ)}).

TABLE 1 PARAMETERS OF THE REFERENCE MODEL Parameter Value Units L 1   μmL_(c)  0.015 μm G 1 × 10⁻⁶ μm γ 1.5 D 2.3 M 10  

To study the feasibility of the proposed approach, a few numericalexperiments were designed. The first experiment was based on syntheticdata and is aimed at inversely identifying only the phases of a surfaceconstructed by the fractal itself. The original surface is shown in FIG.2A and was constructed using Eq. 7 with random phases and the parametersas shown in Table 1. The inversely identified surface using the phasesresulting from Eq. 25 is presented in FIG. 2B. The absolute differencebetween those two surfaces is shown in FIG. 3. It should be noted thatthis difference is very small compared to the magnitude of the surfaceand any discrepancies should be considered as the numerical error of theSVD algorithm. Another interesting remark that is not obvious here isthe fact that the real part of the identified phases from the inversionwhere differing from the ones of the forward problem by 2 πk withk=−1,0,1 showing no deterministically defined selection for one of thesethree values.

The second synthetic experiment involved the identification of both thephases and the γ parameter. An exhaustive search approach was adopted inthis case, as the sensitivity of the SVD inversion relative to the valueof γ is also of interest. For a range of the possible values forparameter γ the inversion of the phases was executed and the value ofthe error function (Eq. 28) was calculated. The error for various valuesof γ is presented on FIG. 4. The smallest value for the error is atγ=1.5, which is the one used originally for the generation of thesurface (Table 1).

Although the previous analysis demonstrates the consistency of theproposed approach, it is much more useful when applied to actualsurfaces. For this reason, two numerical tests are performed based onprofilometric data of an aluminum 6061-T6 alloy surface are presentedhere. The experimentally measured surface for a domain size of 50×50measurements of a domain that is 200×200 μm² is presented in FIG. 5A.

In FIG. 5B the inversely identified surface are presented for the valuesof γ returning the lowest error. The absolute difference between theoriginal surface and the approximated surfaces are presented in FIG. 6,while the value of error for the various values of γ is in FIG. 7. As isshown in FIG. 6 very few areas of the image exceed 10% error. This is anindication suggesting exploration of the possibility to use the W-Mfunction as compression algorithm, which indeed we have alreadyundertaken and present results in the future. On the other hand, FIG. 9demonstrates that when the same number of phases is used for thecharacterization as the ones corresponding to the dimensions of theexperimental data array, the error stays mostly below 0.6% and only afew points reach values in the neighborhood of 1.4%. We consider this tobe a very successful characterization outcome.

Applications of the invention include quantum structure description,material microstructure, materials surface descriptions, materialssurface characterization, image intensity or color space valuerepresentation and image compression, bathymetry representation,geo-spatial elevation representation, acoustic surface representation,electromagnetic surface representation, and cosmological andastrophysical data representation.

FIGS. 8-11 show the efficacy of the invention as applied to threeexemplary applications of the algorithm. FIGS. 8A-B respectively showProfilometry: Aluminum 6061T6 and Analytical Reconstruction of Aluminum6061T6 profilometry for a domain size of 50×50; FIGS. 10A-B respectivelyshow Bathymetry data: Kane Fracture Zone and Analytical Reconstructionof Kane Fracture Zone Bathymetry; and FIGS. 11A-B respectively showImage data: Glacier Canyon and Analytical Reconstruction of GlacierCanyon Image. It is evident from these examples that the inventionsignificantly improves the resolution of the images and differentiationbetween features in the images.

It should be noted that the present invention can be accomplished byexecuting one or more sequences of one or more computer-readableinstructions read into a memory of one or more computers from volatileor non-volatile computer-readable media capable of storing and/ortransferring computer programs or computer-readable instructions forexecution by one or more computers. Volatile computer readable mediathat can be used can include a compact disk, hard disk, floppy disk,tape, magneto-optical disk, PROM (EPROM, EEPROM, flash EPROM), DRAM,SRAM, SDRAM, or any other magnetic medium; punch card, paper tape, orany other physical medium. Non-volatile media can include a memory suchas a dynamic memory in a computer. In addition, computer readable mediathat can be used to store and/or transmit instructions for carrying outmethods described herein can include non-physical media such as anelectromagnetic carrier wave, acoustic wave, or light wave such as thosegenerated during radio wave and infrared data communications.

While specific embodiments of the present invention have been shown anddescribed, it should be understood that other modifications,substitutions and alternatives are apparent to one of ordinary skill inthe art. Such modifications, substitutions and alternatives can be madewithout departing from the spirit and scope of the invention, whichshould be determined from the appended claims. For example, regardingthe array of elevation data described above, it would be readilyapparent to one skilled in the art that it could also be applicable fornon-square domains (e.g. rectangular, con-convex polygonal). Likewise,the data is not limited to just rectangular grids, but also grids ofarbitrary nature.

1. A computer implemented method for directly determining parametersdefining a Weierstrass-Mandelbrot (W-M) analytical representation of arough surface scalar field with fractal character, embedded in a threedimensional space, utilizing pre-existing measured elevation data of arough surface in the form of a discrete collection of data describing ascalar field at distinct spatial coordinates, comprising applying aninverse algorithm to the elevation data to thereby determine theparameters that define the analytical and continuous W-M representationof the rough surface.
 2. The method of claim 1, wherein the surface hasan array of elevation or height measurements z_(ij) ^(e), i, j=1 . . . Kover a square region of size L×L and the parameters are γ and φ_(mn) ofthe surface z(x,y) and the surface is represented by the complexWeierstrass-Mandelbrot (W-M) function W as: $\begin{matrix}{{W(x)} = {\sum\limits_{n = {- \infty}}^{\infty}{{\gamma^{{({D - 2})}n}\left( {1 - ^{{\gamma}^{n}x}} \right)}^{{\varphi}_{n}}}}} & (1)\end{matrix}$ where x is a real variable, and where a two dimensionalprofile obtained from the real part of Eq. 1 is: $\begin{matrix}{{z(x)} = {{{Re}\left\lbrack {W(x)} \right\rbrack} = {\sum\limits_{n = {- \infty}}^{\infty}{{\gamma^{{({D - 2})}n}\left\lbrack {{\cos \; \varphi_{n}} - {\cos \left( {{\gamma^{n}x} + \varphi_{n}} \right)}} \right\rbrack}.}}}} & (2)\end{matrix}$ and where D is the fractal dimension (1<D<2 for lineprofiles), φ_(n) is a random phase that is used to prevent coincidenceof different phases, n is the frequency index and γ is a parameter thatcontrols the density of the frequencies, comprising: applying a SingularValue Decomposition (SVD) algorithm to a refactoring of Eq. (1), therebydetermining said all necessary parameters of the Weierstrass-Mandelbrot(W-M) function describing the surface originally given in terms ofmeasured elevation data.
 3. The method of claim 2, wherein theapplication of the SVD algorithm comprises: representing a least squaressolution p:p=Vy  (3) where a system can be written as:Cp=z and calculating V by the Singular Value Decomposition (SVD) of Cas:UDV ^(T) =C  (4) where y is a vector defined as y_(i)=b_(i)′/d_(i),b={b_(i)} is a vector given by:b=U ^(T) p  (5) and d _(i) is the ith entry of the diagonal of D.
 4. Themethod of claim 1, wherein the data is profilometry data for materials.5. The method of claim 1, wherein the data is topography data forplanetary terrestrial areas.
 6. The method of claim 1, wherein the datais bathymetry data for ocean bottom areas.
 7. The method of claim 1,wherein the data is radar topography data for planetary surfacedescriptions.
 8. The method of claim 1, wherein the data is imageintensity or color space encoding data.
 9. The method of claim 1,wherein the data is topography of acoustic or electromagnetic scatterersurfaces.
 10. A computer software product for directly determiningparameters defining a Weierstrass-Mandelbrot (W-M) analyticalrepresentation of a rough surface scalar field with fractal character,embedded in a three dimensional space, utilizing pre-existing measuredelevation data of a rough surface in the form of a discrete collectionof data describing a scalar field at distinct spatial coordinates, theproduct comprising a physical computer-readable medium including storedinstructions that, when executed by a computer, cause the computer toapply an inverse algorithm to the elevation data to thereby determinethe parameters that define the analytical and continuous W-Mrepresentation of the rough surface.
 11. The computer software productof claim 10, wherein the surface has an array of elevation or heightmeasurements z_(ij) ^(e), i, j=1 . . . K over a square region of sizeL×L and the parameters are γ and φ_(mn) of the surface z(x,y) and thesurface is represented by the complex Weierstrass-Mandelbrot (W-M)function W as: $\begin{matrix}{{W(x)} = {\sum\limits_{n = {- \infty}}^{\infty}{{\gamma^{{({D - 2})}n}\left( {1 - ^{{\gamma}^{n}x}} \right)}^{{\varphi}_{n}}}}} & (1)\end{matrix}$ where x is a real variable, and where a two dimensionalprofile obtained from the real part of Eq. 1 is: $\begin{matrix}{{z(x)} = {{{Re}\left\lbrack {W(x)} \right\rbrack} = {\sum\limits_{n = {- \infty}}^{\infty}{{\gamma^{{({D - 2})}n}\left\lbrack {{\cos \; \varphi_{n}} - {\cos \left( {{\gamma^{n}x} + \varphi_{n}} \right)}} \right\rbrack}.}}}} & (2)\end{matrix}$ and where D is the fractal dimension (1<D<2 for lineprofiles), φ_(n) is a random phase that is used to prevent coincidenceof different phases, n is the frequency index and γ is a parameter thatcontrols the density of the frequencies, and wherein the instructionsinclude applying a Singular Value Decomposition (SVD) algorithm to arefactoring of Eq. (1), thereby determining said all necessaryparameters of the Weierstrass-Mandelbrot (W-M) function describing thesurface originally given in terms of measured elevation data.
 12. Thecomputer software product of claim 11, wherein the instructionsincluding the application of the SVD algorithm comprise: representing aleast squares solution p:p=Vy  (3) where a system can be written as:Cp=z and calculating V by the Singular Value Decomposition (SVD) of Cas:UDV ^(T) =C  (4) where y is a vector defined as y_(i)=b_(i)′/d_(i),b={b_(i)} is a vector given by:b=U ^(T) p  (5) and d _(i) is the ith entry of the diagonal of D. 13.The computer software product of claim 10, wherein the data isprofilometry data for materials.
 14. The computer software product ofclaim 10, wherein the data is topography data for planetary terrestrialareas.
 15. The computer software product of claim 10, wherein the datais bathymetry data for ocean bottom areas.
 16. The computer softwareproduct of claim 10, wherein the data is radar topography data forplanetary surface descriptions.
 17. The computer software product ofclaim 10, wherein the data is image intensity or color space encodingdata.
 18. The computer software product of claim 10, wherein the data istopography of acoustic or electromagnetic scatterer surfaces.